A Parameter-Free Associative Classification Method

In many application domains, classification tasks have to tackle multiclass imbalanced training sets. We have been looking for a CBA approach (Classification Based on Association rules) in such difficult contexts. Actually, most of the CBA-like methods are one-vs-all approaches (OVA), i.e., selected rules characterize a class with what is relevant for this class and irrelevant for the union of the other classes. Instead, our method considers that a rule has to be relevant for one class and irrelevant for every other class taken separately. Furthermore, a constrained hill climbing strategy spares users tuning parameters and/or spending time in tedious post-processing phases. Our approach is empirically validated on various benchmark data sets.